Max Robotics — Global Service Robot Directory + US Certification

About

Morgan Nilsen is a researcher specializing in robotized laser beam welding, with a particular focus on process monitoring, quality control, and automation of closed-square-butt joint welding — a technically demanding area where even microscopic beam misalignment can cause serious structural defects. Nilsen's work addresses one of the field's most persistent challenges: reliably detecting and tracking near-invisible zero-gap joints in real time during automated welding operations. Among Nilsen's most impactful contributions is the application of wavelet analysis to photodiode signals for detecting beam offsets, garnering 25 citations since 2018, alongside a robust camera-based joint tracking system for curved geometries (17 citations). Together, these works represent a significant advancement in closed-loop process control for laser welding systems. Nilsen has also explored integrated vision-based seam tracking and, more recently, deep learning approaches — including convolutional neural networks for gap width classification and tack weld detection — signaling a forward-looking shift toward AI-driven manufacturing intelligence. With a research portfolio spanning optical sensing, computer vision, and machine learning applied to precision welding, Nilsen's contributions are shaping smarter, more reliable robotic welding systems for modern industrial production.